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Multi-region brain transcriptomes uncover two subtypes of aging individuals with differences in Alzheimer risk and the impact of <i>APOEε4</i>.

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  1. [1] § STAR★Methods › Method details › Integrating transcriptomes from multiple brain regions using sparse multiple CCA ↔ dis-cluster.R, lines 1–39 · score 0.73 · sparse multiple canonical, multi CCA, Discovery cohort, brain regions, correlated, meta clusters
  2. [2] § STAR★Methods › Method details › Integrating transcriptomes from multiple brain regions using sparse multiple CCA ↔ dis-cluster.R, lines 1–39 · score 0.69 · MultiCCA, Discovery cohort, sparse, correlations, canonical, brain

Paper

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The authors' code

R · 92 lines · 3.3 KB · no license · 2 matches

  1. ##################################################
  2. # Discovery cohort #
  3. # Sparse multiple canonical correlation analysis #
  4. # + K-means + NMF meta-clustering #
  5. ##################################################
  6. library(PMA)
  7. library(NMF)
  8. set.seed(1234)
  10. ##################################################
  11. # Toy data: three brain regions #
  12. ##################################################
  13. n_samples <- 459
  14. n_genes_DLPFC <- 18629
  15. n_genes_AC <- 19147
  16. n_genes_PCC <- 19017
  18. # Latent subject factor
  19. u <- matrix(rnorm(n_samples), ncol = 1)
  21. # Region-specific loading patterns
  22. v_DLPFC <- matrix(c(rep(1, 25), rep(0, n_genes_DLPFC - 25)), ncol = 1)
  23. v_AC <- matrix(c(rep(0.5,25), rep(0, n_genes_AC - 25)), ncol = 1)
  24. v_PCC <- matrix(c(rep(0.5,25), rep(0, n_genes_PCC - 25)), ncol = 1)
  26. # Simulated expression matrices (samples x genes)
  27. gx_DLPFC <- u %*% t(v_DLPFC) + matrix(rnorm(n_samples * n_genes_DLPFC), nrow = n_samples)
  28. gx_AC <- u %*% t(v_AC) + matrix(rnorm(n_samples * n_genes_AC), nrow = n_samples)
  29. gx_PCC <- u %*% t(v_PCC) + matrix(rnorm(n_samples * n_genes_PCC), nrow = n_samples)
  31. ##################################################
  32. # Sparse multiple canonical correlation analysis #
  33. ##################################################
  34. xlist <- list(gx_DLPFC, gx_AC, gx_PCC)
  36. # Choose penalties via permutation (low nperms here for illustration)
  37. perm.out <- MultiCCA.permute(xlist, nperms = 10, type = "standard")
  39. cca_fit <- MultiCCA(xlist, type="standard", penalty=perm.out$bestpenalties, ws=perm.out$ws.init, ncomponents=10, standardize = TRUE)
  41. # Canonical weight vectors
  42. w_DLPFC <- cca_fit$ws[[1]]
  43. w_AC <- cca_fit$ws[[2]]
  44. w_PCC <- cca_fit$ws[[3]]
  46. # Canonical component scores (samples x components)
  47. cv_DLPFC <- gx_DLPFC %*% w_DLPFC
  48. cv_AC <- gx_AC %*% w_AC
  49. cv_PCC <- gx_PCC %*% w_PCC
  51. #########################################
  52. # K-means clustering within each region #
  53. #########################################
  54. region_data <- list(
  55. DLPFC = cv_DLPFC,
  56. AC = cv_AC,
  57. PCC = cv_PCC
  58. )
  60. clusters <- list()
  61. centroids <- list()
  62. for (region in names(region_data)) {
  63. x <- region_data[[region]]
  64. kfit <- kmeans(x, centers = 2)
  65. clusters[[region]] <- data.frame(cluster = kfit$cluster)
  66. centroids[[region]] <- kfit$centers
  67. }
  69. ##################################################
  70. # Meta-clustering across regions using NMF #
  71. ##################################################
  72. # Binary cluster membership matrix:
  73. # rows = region-specific clusters
  74. # cols = subjects (aligned by rownames)
  75. X <- rbind(
  76. DLPFC1 = as.numeric(clusters$DLPFC$cluster == 1),
  77. DLPFC2 = as.numeric(clusters$DLPFC$cluster == 2),
  78. AC1 = as.numeric(clusters$AC$cluster == 1),
  79. AC2 = as.numeric(clusters$AC$cluster == 2),
  80. PCC1 = as.numeric(clusters$PCC$cluster == 1),
  81. PCC2 = as.numeric(clusters$PCC$cluster == 2)
  82. )
  83. colnames(X) <- rownames(clusters$DLPFC)
  85. # NMF to identify shared “meta-clusters” across regions
  86. k_meta <- 2
  87. nmf_fit <- nmf(X, rank = k_meta, method = "lee", seed = 123456, nrun = 10)
  89. # Meta-cluster assignment per subject
  90. H <- coef(nmf_fit) # meta-clusters x subjects
  91. meta_cluster <- apply(H, 2, which.max) # length = n_samples
  92. names(meta_cluster) <- colnames(H)

dis-cluster.R at commit a1e226c, no license · at the source

Overview

Authors: Annie J Lee1,2,3,4, Yiyi Ma1,2,4, Lei Yu5, Robert J Dawe5, Cristin McCabe6, Konstantinos Arfanakis5,7, Richard Mayeux1,2,3, David A Bennett5, Hans-Ulrich Klein1,2,4, Philip L De Jager1,2,4
ORCID iDs: Annie J Lee, Konstantinos Arfanakis, Philip L De Jager
  1. Department of Neurology, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
  2. Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
  3. The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
  4. Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
  5. Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
  6. Broad Institute, Cambridge, MA 02142, USA
  7. Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Institutions: Columbia University Irving Medical Center (United States); Columbia University (United States); Rush University Medical Center (United States); Broad Institute (United States); Illinois Institute of Technology (United States)
Journal: iScience, volume 29, issue 8, article 117038
Dates: received 26 September 2025; accepted 16 July 2026; published online 10 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.117038 · PMID 42620705 · PMCID PMC13486859 · OpenAlex W7202124883
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Alzheimer’s disease, aging brain, multi-region transcriptomics, unsupervised clustering, canonical correlation analysis, cognitive decline, molecular vulnerability
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: NIA NIH HHS (P30 AG066462, R01 AG036836, U01 AG061356, U19 AG078109, P30 AG010161, R01 AG015819, U01 AG046152, R01 AG017917, K01 AG084849); National Institute on Aging (P30AG066462, U19AG078109, K01AG084849, R01AG036836, P30AG10161, R01AG17917, R01AG15819, U01AG61356, U01AG046152); National Institutes of Health; National Alzheimer&apos;s Coordinating Center; The Thompson Family Foundation Inc; American Association for Cancer Research; Carol and Gene Ludwig Family Foundation
Citations: cited by 3 papers (Europe PMC); 52 references in the paper

Abstract

The heterogeneity of the aging population suggests the existence of molecularly distinct subgroups that differ in vulnerability to Alzheimer’s disease (AD), yet this population structure remains poorly defined. We performed unsupervised clustering of multi-region brain transcriptomes to assess whether integrating data across regions involved in cognition could uncover such subgroups. Canonical correlation-based analysis in a discovery cohort of 459 participants with RNA-sequencing data from three regions (dorsolateral prefrontal cortex, posterior cingulate cortex, and anterior caudate), followed by replication in 690 additional participants with partial data, identified two meta-clusters (MC-1 and MC-2). These groups differed in cognitive trajectories, with MC-2 showing a three-year delay in dementia onset relative to MC-1. This may reflect, in part, a greater impact of tau pathology on neuronal chromatin architecture, white matter loss, and APOEε4-related cognitive decline in MC-1. These findings reveal a molecular population structure of the aging brain that modulates vulnerability and resilience to AD and may inform targeted therapies and trials.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

anniejlee/multi-region-transcriptomes-aging-subtypes

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a1e226cf4e91a6873c21f01b2dccb840cd23a3d4, 16 December 2025
Languages: R (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/anniejlee/multi-region-transcriptomes-aging-subtypes

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data and code availability

The normalized RNA-seq data from the three brain regions are available through the AD Knowledge Portal (https://adknowledgeportal.org). Data described in this manuscript are available on Synapse (Synapse: syn25741873) and are accessible according to the AD Knowledge Portal data access policies. Accession numbers and repository information are listed in the key resources table.

All original analysis code has been deposited in GitHub and is publicly available as of the date of publication. Repository information is listed in the key resources table.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

  • Authors: added Annie J Lee (0000-0003-3726-6024); Philip L De Jager (0000-0002-8057-2505); removed Annie J Lee; Philip L De Jager

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 7 keywords, 7 funders, 51 references.

Cite

This paper

Lee, A. J., Ma, Y., Yu, L., Dawe, R. J., McCabe, C., Arfanakis, K., Mayeux, R., Bennett, D. A., Klein, H.-U., & De Jager, P. L. (2026). Multi-region brain transcriptomes uncover two subtypes of aging individuals with differences in Alzheimer risk and the impact of &lt;i&gt;APOEε4&lt;/i&gt;. iScience, 29(8), 117038. https://doi.org/10.1016/j.isci.2026.117038

BibTeX

@article{lee2026multi,
author = {Lee, Annie J and Ma, Yiyi and Yu, Lei and Dawe, Robert J and McCabe, Cristin and Arfanakis, Konstantinos and Mayeux, Richard and Bennett, David A and Klein, Hans-Ulrich and De Jager, Philip L},
title = {{Multi-region brain transcriptomes uncover two subtypes of aging individuals with differences in Alzheimer risk and the impact of \&lt;i\&gt;APOEε4\&lt;/i\&gt;}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {8},
pages = {117038},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117038},
url = {https://doi.org/10.1016/j.isci.2026.117038},
pmid = {42620705},
pmcid = {PMC13486859}
}

RIS

TY - JOUR
AU - Lee, Annie J
AU - Ma, Yiyi
AU - Yu, Lei
AU - Dawe, Robert J
AU - McCabe, Cristin
AU - Arfanakis, Konstantinos
AU - Mayeux, Richard
AU - Bennett, David A
AU - Klein, Hans-Ulrich
AU - De Jager, Philip L
TI - Multi-region brain transcriptomes uncover two subtypes of aging individuals with differences in Alzheimer risk and the impact of &lt;i&gt;APOEε4&lt;/i&gt;
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/10
VL - 29
IS - 8
SP - 117038
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117038
UR - https://doi.org/10.1016/j.isci.2026.117038
LA - en
ER -

CSL-JSON

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